How to use a Generative Adversarial Network to generate images for developmental analysis?
A generative adversarial network is probably not the best approach for generating the images desired.  We can assume from the comments that the data is not collected.  That's a good thing, because a set of rasterized images, labeled with student age or grade is an inferior input form.

It appears that access to a student population is planned or already negotiated, which is also good.

Although the drawing, as it is being drawn, is seen through each student's eyes, the primary features correlated with drawing skill development is motor control, shape formation, and color choice.  If the sheet of paper is placed over a drawing tablet, the tablet's incoming USB stream events are captured to a file, and the color selection is somehow recorded or automatically determined by having students hold the pencil or crayon up to the computer's camera before using it, a much better in natura input stream can be developed.

Pre-processing can lead to an expression of each drawing experience as a sequence of events arranged in temporal order with the following dimensions for each event.


Relative time from the instruction to draw in seconds
Color
Nearest x grid
Nearest y grid
Pressure


Determining color from camera input may be developed using LSTM approaches.

The dimensions of the label for each of these sequences would be those demographics and rankings that would most closely correlated with developmental stages.


Student age
Student gender
Curriculum grade (-1, 0, 1, 2, ... 12, where -1 is preschool and 0 is kindergarten)
Identifier of the drawing instructions given to the class
Grade ranking of the student in the class


The micro-analysis attached to each ELEMENT in the sequence includes these additional dimensions.


Drawing rate of the utensil given by $r = \frac {\sqrt{(x - x_p)^2 + (y - y_p)^2}} {t - t_p}$ where the subscript p indicates the values are drawn from the previous event in the sequence.
Drawing direction given by $\theta = \arctan (x - x_p, \; y - y_p)$
Curvature $\kappa$ calculated using cubic splines or some other data fitting approach
FFT spectrum $\vec{a}$ and Lyapunov exponent $\lambda$ applied to auto-correlation results


This is a modification of the system Google uses to synthesize speech, based on the WaveNet design. In the diagram, the residual function is defined as follows.

$z = \tanh \, (W_{f,k} x + V_{f,k} y) \, \odot \, \sigma \, (W_{g,k} x + V_{g,k} y)$

The development required is that the $\vec{a}$ must now be accompanied with scalars $r, \theta, \kappa, and \lambda$, but the resulting drawings are likely to have many of the hand-eye developmental features of the examples.